How Testrig Technologies Delivered an AI-Driven QA Transformation for a Risk & Compliance Platform

The platform handled critical user workflows with strict validation requirements, multiple third-party integrations, and frequent Agile releases. Even small defects could lead to major disruptions and compliance risks.

Some of the key challenges were:

• High dependency on manual testing for complex workflows
• QA involvement often happened late in the development cycle, leading to defects being found close to release
• Requirements were sometimes unclear, creating interpretation gaps between teams
• Large manual effort was spent on repetitive tasks like test case creation, updates, and defect documentation
• Growing regression scope made testing cycles longer and harder to manage
• Increasing backend and API complexity made validation more difficult
• Flaky and slow UI automation reduced confidence in automated regression
• Limited visibility into test execution results and release readiness
• Lack of strong alignment between development and QA practices

There was a clear need to move toward a faster, more reliable, and intelligent testing strategy.

Instead of attempting a complete overhaul in one go, a phased approach was adopted. Each phase focused on solving specific challenges while building a stronger foundation for the next stage of QA maturity.

AI tools were gradually introduced to improve quality, reduce manual effort, and increase overall testing efficiency.

The transformation started by improving manual testing practices and involving QA earlier in the development lifecycle.

What was done:

• QA was involved in requirement reviews, story refinement, and sprint planning
• Requirements and acceptance criteria were validated early for clarity and completeness
• AI-assisted summarization helped QA understand complex requirements faster
• AI was used to identify missing flows, edge cases, and requirement gaps
• AI-assisted drafting of functional and BDD-style test cases reduced repetitive documentation work
• AI support was used to improve defect reporting by structuring summaries, reproduction steps, and expected vs actual behavior
• AI also supported API understanding and validation scenario creation during backend testing
• Reduced repetitive effort allowed QA teams to spend more time on exploratory and risk-based testing

Impact:

• Reduced late-stage defect discovery
• Improved requirement understanding and reduced interpretation gaps
• Lower manual effort spent on repetitive testing tasks
• Faster and clearer defect reporting
• Better API and backend validation coverage
• Increased focus on exploratory testing for complex workflows

Once the manual QA process became stronger, the next step was to establish a stable automation foundation.

What was done:

• Built a Selenium-based automation framework using C# and BDD
• Structured the framework to closely match the product’s development architecture
• Introduced reusable components and clear test design patterns
• Enabled better collaboration through behavior-driven scenarios that were easier for teams to understand

Impact:

• Created a strong and maintainable automation foundation
• Improved readability and consistency of automated test cases
• Reduced framework maintenance effort
• Improved alignment between QA and development teams

After establishing a stable automation base, the focus shifted to API-level validation for faster feedback and stronger backend coverage.

What was done:

• Introduced Playwright for API automation using TypeScript
• Continued using BDD for consistency across testing layers
• Leveraged AI tools like GitHub Copilot for writing and optimizing test scripts
• Used AI-assisted PR reviews to improve code quality and consistency
• Expanded automated API validation for positive, negative, and edge-case scenarios

Impact:

• Faster test creation with reduced scripting effort
• Better API coverage and earlier bug detection
• Improved automation code quality
• Stronger confidence in backend integrations

The next step was modernizing UI automation by moving away from Selenium to a faster and more reliable framework.

What was done:

• Migrated existing UI tests to Playwright end-to-end framework
• Used AI-assisted migration workflows to speed up framework conversion
• Reduced dependency on brittle locators and manual waits
• Unified UI and API automation under one modern testing stack

Impact:

• Significant reduction in flaky tests
• Faster and more reliable execution
• Lower maintenance effort for automated tests
• Improved consistency across automation layers

With the testing framework modernized, the focus shifted to improving execution speed, visibility, and release readiness.

What was done:

• Implemented parallel execution (sharding) using GitHub Actions
• Integrated automated reporting and notifications
• Automated test cycle creation using Zephyr and JIRA APIs
• Added real-time execution updates through Microsoft Teams
• Optimized pipelines for faster feedback and better release visibility

Impact:

• Drastic reduction in regression execution time
• Faster release cycles with higher confidence
• Better visibility into testing progress and release readiness
• Improved communication and traceability across teams

To further improve efficiency, several integrations were introduced:

• Email validation using Mailpit and Outlook APIs
• Automated test cycle creation using Zephyr and JIRA APIs
• Real-time execution updates via Microsoft Teams
• AI support through GitHub Copilot, OpenAI Codex / LLM-based workflows, and Playwright MCP for script generation, migration, and execution optimization

These integrations reduced manual effort, improved communication, and increased overall testing efficiency.

• C# with Visual Studio
• TypeScript with Visual Studio Code
• Selenium with BDD
• Playwright (UI + API) with BDD
• Postman & Swagger
• GitHub Actions (CI/CD)
• Microsoft Teams (notifications)
• Zephyr & JIRA (test management)
• Grafana k6 (performance testing)
• GitHub Copilot (test script generation, PR reviews)
• OpenAI Codex / LLM-based workflows (test migration, code transformation)
• Playwright MCP(AI-assisted migration and execution optimization)

• End-to-end testing coverage across manual, API, and UI layers
• Reduced manual effort through AI-assisted workflows
• Faster requirement understanding and test design
• Stronger API and backend validation coverage
• More stable and reliable automation suite
• Faster regression cycles through parallel execution
• Better visibility into release readiness
• Improved collaboration between QA and development teams
• Strong adoption of AI to improve productivity and quality